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Data Services: Quality Data sets Single Point of contact for Global Weather Data We carry tens of thousands of historical weather data sets from around the world. We quality-control thousands of data sets every day. We are the dominant settlement agent for over-the-counter weather risk contracts around the world. We consider weather data as a form of financial market data Speedwell SuperPack ® makes available an unprecedented range of quality historical weather data and weather data feeds for a single annual fee. Speedwell Cleaned Data Cleaned data is data that has been processed to fill missing values and correct erroneous observations. The end result is a data set that is ready to be used for analysis. We clean weather data from thousands of weather stations every day. Speedwell’s proprietary cleaning methodologies use a mixture of automated processing but always overseen with meteorological expertise. 3

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Forecast Products: Ensemble Forecasts Speedwell Site-Specific Ensemble Forecasts Speedwell site-specific downscaled ensemble forecasts for single sites and weighted baskets are available for over 2,000 sites across the world covering 15-day and monthly periods. The ensemble forecast is a fully downscaled probabilistic forecast which inherently captures information that is normally lost in a traditional deterministic forecast: the uncertainty of the forecast at each time step. Forecast Dashboard FastCast ® Forecasts Ultra-fast graphical representation of the change in forecast from the previous run. Updated as each time step becomes available. WDD: U.S. Weighted Degree Days Uses weather data and forecasts combined with regional population data to estimate the EIA natural gas storage and withdrawal statistics. Warmer or Colder? In this example we show the ECMWF Operational forecast change for four European sites simultaneously. Also available: US cities and power regions View the difference between successive model runs or previous runs: Diff 6hr, 12hr, Diff 24hr, Diff 48hr, Diff 72hr Ensemble Forecasts can be viewed through the on-line forecast viewer. A number of graphing options can be chosen. This example shows the last two weeks of actual data and the forecast in “box-and-whiskers” form The Dashboard is user- configurable allowing any number of sites to be presented in any order in a numerical grid with drill-down options 5

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Commodity Monitor Tool An interface providing access to regional crop baskets for agri-products and population weighted baskets for understanding energy demand. The tool shows the behaviour of recent weather vs normals and providing ensemble forecasts based on both the ECMWF and GFS models. 6

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Speedwell as Settlement Agent Speedwell is the dominant provider of settlement data for weather risk contracts world-wide. Our involvement avoids problems that might arise even when using data sourced from national met offices. These include: Unexpected closure of weather stations Missing data points Failure of instruments under extreme conditions Problems arising from odd reporting conventions such as multi-day rainfall reports Late data provision Data reporting errors Weather Station Installation We have many years of experience in the data requirements necessary for cost-effective weather risk placement. A parallel weather installation can improve the quality of data used in weather risk transactions and may have a positive impact on risk premia. Weather Risk Placement/Consultancy: Speedwell Weather Derivatives (SWD) is the regulated subsidiary of Speedwell Weather Limited. SWD have many years of experience in structuring and advising on the placement of weather risk. We are also able to provide independent opinions on the valuation of individual weather risk deals and to value portfolios of weather risk contracts to satisfy independent audit requirements. Services / Consultancy Speedwell Weather Station: Armerillo, Chile installed to support settlement data for a large rainfall hedge SWD is authorised and regulated by the Financial Conduct Authority 7

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Weather Risk Settlement Services: World Bank Uruguay Transaction The largest to date

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What exactly is weather data and where does it come from ? Weather observations are not new, they have been around since the middle ages ! William Merle made detailed diary observations in Oxford between 1337 and 1344 during The Little Ice Age Modern day observations come from a variety of sources and record a wide range of meteorological parameters Who observes the weather ? We all do ! Recorded measurements come from a variety of sources: National Met Services, Hydromet networks, ships, aircraft, satellite, buoys, weather Radar, amateur networks, private institutions and individuals. Measurements are made at both fixed and mobile locations – coverage is not consistent Extract from William Merle’s weather diary c1393 Observations are continuously recorded around the globe in order to forecast the weather

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Data provenance Data sources include – National Met Services, hydro-meteorological services, agri-networks, academic bodies, observatories, airport operators, Coop observing networks, schools, private companies, private individuals, our own instruments. It is important to know the origin of the data so that the user can ask questions later. It is important to know the origin of the data so that the user can match any important metadata to the series. Knowing where the data originates helps us to better understand how it has been recorded, stored, processed and any changes to the method of measurement. All of the above sources of data can produce high quality weather data as well as data that is unfit for our purposes. Speedwell Weather use data under licence from official and quality sources only.

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Data - Metadata In order to fully appreciate the data series and understand how it has been recorded, the station metadata is an essential additional information series. Should describe reporting conventions used (times, period). Should describe changes in instruments over time (updated technology, changes from manual readings to fully automated sampling. Provide a record of changes in location – site moves. Should if possible provide a plan of the site and changes over time. Confirm the reporting units for each element/parameter. Extract of station metadata for instruments used at Berlin Dahlem – courtesy DWD

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Data quality Weather measurements are made using wide ranging array of instruments, not all record to the exacting standards of the World Meteorological Organisation (WMO).

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Some can be good, some less so – which is the official site ? A private network installation (Speedwell, Chile) Official US COOP site (NCDC)

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Weather Data Conventions We tend to think of weather data as being unambiguous. This is not the case. Care needs to be taken. For example a daily maximum temperature can be 12 hour max/24 hour max and apply to different measurement periods We are scrupulous in storing data to respect different reporting conventions. We log those conventions. We can supply a document detailing reporting conventions around the world. A deep understanding of data conventions is necessary before data can be quality controlled, otherwise any comparison is flawed.

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15 Observation Convention All data is observed from midnight to midnight (the calendar day) In reality, observation convention varies from country to country and network to network. In general it can be assumed that the observation of a given variable (such as daily maximum temperature) will be consistent across an entire network. The convention is set by the network owner. When asked, data vendors should be able to describe the observation convention for all datasets. Why is this important?  When comparing / merging datasets, it is important to compare like with like.  When verifying forecasts, make sure the observation convention matches the forecast day convention.  For weather risk contracts, reporting conventions can have a material impact on settlement values. In the UK and France Climate TMax & TMin are observed over different 24hr periods! Did You Know? Many US COOP obs are taken at 7am because that is when observers wake! Common Misconceptions About Weather Observations

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16 Daily Observations All “daily” observations represent a full day (24 hour period) The truth is that “Daily” data, especially those derived from the SYNOP network sometimes only represents a partial day. A nighttime TMin or daytime TMax is common practice. When asked, a data vendor should be able to explain the convention used. Why is this important?  Some examples - people consume electricity 24 hours a day - frost events damage plants at all hours - crops don’t care when it rains as long as it rains Common Misconceptions About Weather Observations

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17 Common Misconceptions About Weather Observations Data Quality Missing and erroneous observations are uncommon Why is this important?  Missing values make an analysis difficult if not impossible. You cannot ignore missing values.  An erroneous 100mm of rain can make the difference between drought and flood.  Often erroneous or missing values are the most important values: observations that arise in weather extremes are more likely to fail The truth is that missing values and erroneous values are common. The best solution is to only use CLEANED data. This is data where missing and erroneous values are filled and replaced.

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18 Understanding the limitations of the GSOD data set The GSOD or “Global Summary of Day” data set is an important data resource and is freely available from NWS/NCDC. However, the usability of this data varies depending upon the intended analysis to be performed. In certain circumstances the data can be very useful while in others it needs to be used with caution. The difficulty is not knowing when the data can be trusted. We caution that this data should never be relied upon to price weather risk contracts and should be avoided where possible when analyzing weather risk for commodity transactions, crop yield models, as well as other sensitive analyses. Shown above is daily precipitation for Jiulong, China for The blue line is Speedwell Cleaned Climate data which is overlaid on top of GSOD data in orange. In this period GSOD over estimates rainfall by 24%. Shown below is the daily wind for Dublin Airport, Ireland. The blue line is official quality controlled data from the Irish Met Office. The red is GSOD. We can see that there is a two-year period where the GSOD data is incorrect. Minor differences are found in other years.

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So far we have been considering data for a single location In terms of data, there is much more than a single series of data for a given location. Examples include: Satellite imagery providing large area data for such variables as temperature, cloud cover, solar radiation, potential precipitation, fog… Rainfall Radar which can provide information at high resolutions, both temporal and distance Model re-analysis which is a uniform gridded snap-shot taken from the global and regional forecast model output (in essence a T+0 forecast field)

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Regional weather data products Large scale weather data products can be very helpful to infill data sparse regions and to assist in the quality control of ground truth measurements. Each of these products, whilst being extremely useful and in general consistent over time do have limitations: All of these products require fine calibration, so are susceptible to bias Satellite imagery often cannot penetrate to the surface due to cloud cover/obscuration Rainfall radar contains ‘blind spots’ – especially in hilly terrain or very close to the earth’s surface Reanalysis products are constantly evolving in line with forecast model improvements – these are also highly sensitive to calibration and require very high quality data input Each time a reanalysis data set is created you need to re-process the entire history, or risk finding step changes in any series produced

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The quality of meteorological observations varies significantly Missing / erroneous observation are common place A lot of weather data available in public archives is stored in an inconsistent manner and is of low quality Speedwell has invested heavily in software, data and human resources to quality control weather data Fundamentals of a proper data cleaning (1)Organization (2)Redundancy (3)Flexibility (4)Human interaction (5)Transparency Fundamental to satisfying the above is the implementation of software systems infrastructure...but data cleaning cannot and should NOT be FULLY automated Part of the Speedwell Data cleaning process diagram Data Cleaning

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Fundamentals of a proper data cleaning (1)Organization (2)Redundancy -data sources -testing -estimates -delivery (3)Flexibility (4)Human interaction (5)Transparency Data sources bring in as much as possible and keep what is useful. Typical processing includes: Climate data (daily / hourly), Synoptic data, METAR, ECMWF forecast data, climatology If one source fails there are others Testing no one test is applicable for all situations. - comparison against itself -physical consistency -statistical probability -comparison against neighbors -Observations are compared against the median of a basket of proxies and the MAD (median absolute deviation). If the observation is statistically different from the surrounding stations it is sent to the filling process Estimates (filling) Why have one when you can have many? Useful for more in-depth manual analysis Data delivery - Multiple FTP deliveries - 24-hour support - logging of all deliveries -Description of data quality and type Data Cleaning..Redundancy A fundamental pre-requisite for effective data cleaning is access to a library of weather data providing access to near by sites allowing plausibility testing for the site being cleaned. Speedwell Weather maintains a very large inventory of weather data for over 50 different weather elements. This is all warehoused by us in a manner that fully respects differing data types (Synoptic/Climate, Cleaned/Raw etc) with a full audit trail. This allows us to document data point changes which may occur when national met offices change data records to reflect their internal QC procedures. We also use satellite and radar data

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The series below is a daily average wind speed series, which on first inspection passes basic quality checks, However when the human eye puts this under scrutiny, a serious error is detected – this proved to be an incorrect unit conversion within the original archive By locating the original coded observations and re-processing, Speedwell is able to produce an improved series, with any missing or incorrect values addressed The incorrect series (blue) with the correct data (yellow) Data Cleaning the human eye test

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Weather forecasts are also types of data, these can take many formats; graphical, time-series, arrays, GRIB, gridded, site/region etc. Forecasts can be delivered in many formats to meet user requirements, however they must include similar information to observed data to be useful: Forecast Data Date time of issue Model source (ECMWF, GFS, SWD) Model run time (00, 06, 12, 18 UTC) Type of model (RAW, Site specific) Deterministic or ensemble Unique site ID (WMO, WBAN, SRCID) Element description Time periods (daily, hourly) Units used Location

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Data can be complex (multiple sources, formats, units, conventions ) Understand the origin of the data before using – contact the data vendor or supplier Importance of metadata (units, environmental and instrument changes / moves) Is the data raw or processed, how can I tell ? Is the data fit for purpose ? Does this location continue to record and report (important if you want to trade) Will the data be homogenous with surrounding or over time ? Does the data format work with my system ? Summary Speedwell Weather in partnership with ZE and the ZEMA application suite address all of the above issues to enable easy integration of quality weather data in to the user’s everyday business